ABSTRACT Circadian (daily) rhythms play a significant role in the efficacy and tolerability of cancer treatments in both rodents and humans. Disruption of circadian rhythms disturbs sleep and has been shown to result in poorer survivorship rates in cancer patients. Furthermore, evidence suggests that timing treatments and interventions with circadian considerations can improve outcomes. While knowledge of internal time holds much potential for the treatment of disease, measuring circadian rhythms in the lab can be time-consuming and expensive. One's typical behavior (e.g. normal wake and bed times) can provide a first-order estimate of circadian state, but individual genetic variation and disruption of the circadian clock from irregular light exposure will throw off this estimate. Light at night from screens, a particularly important factor to consider in monitoring circadian rhythms, is now more common and disruptive than ever. We have developed mobile applications for estimating circadian rhythms in a non-invasive way using mathematical models. The apps track motion and activity and use these factors to predict both light and the user's internal time in a way that accounts for disruptions to one's ?normal? schedule. We propose to develop a mobile app for cancer patients that recommends lighting interventions to correct disrupted circadian rhythms as well as the best times for drug consumption for optimal circadian effect. We furthermore propose to integrate this system into the home environments of patients to improve compliance. The proposed work will be directed toward adapting the math models and algorithms underlying our past work into a tool for cancer patients. The outcome will be a prototype app that tracks patient circadian rhythms and suggests interventions to steer their circadian clocks back in sync with their environment, promoting health and managing insomnia. A Phase I SBIR grant would be used to develop the app and assess its potential in a limited usability trial. A Phase II SBIR would extend the work by refining the app and testing it in a clinical trial.